Generative Design with Multi-Objective Optimization of a Formula SAE Chassis

This work presents a Generative Design methodology integrated with Multi-Objective Optimization applied to the development of a Formula SAE vehicle chassis. The research aims to reduce reliance on human intuition during the early stages of design by utilizing parametric modeling in Rhinoceros 3D and Grasshopper, coupled with the SPEA-2 evolutionary algorithm. The optimization problem considers three conflicting objectives: minimizing total tube length, maximizing internal clearance, and maximizing aesthetic quality based on the golden ratio. Following the Multi-Objective Optimization Design Procedure, the methodology generates a Pareto Front to analyze trade-offs. The results demonstrate the generation of structurally valid and aesthetically refined configurations, expanding the solution space and enabling an integrated analysis of functional and visual criteria within a CAD environment.